CCRVB-39: Franchini VB043B HTLV chimera (Sarkis) - FOR MS

Virus Variables/Viral Score (Fig 2)

Fig 2c heatmap

Viral Score Calcs

For each timepoint, animal:

1. Calculate tp_score across all virus variables:

\(\text{tp_score__WeekXX}=\frac{\text{gag_PCR}_{\text{WeekXX }} + \text{ orfI_II_PCR}_{\text{WeekXX }} + \text{ number_of_bands}_{\text{WeekXX }}/\text{number_of_bands}_{\text{MAX }} + \text{ log}_{10}(\text{p24_titer}_{\text{WeekXX}}) /log_{10}(\text{p24_titer}_{MAX})}{TotalNumberOfVirusVariables}\)

• each virus variable has range of 0-1
• each tp_score__WeekXX has range of 0-1

2. Calculate fracVars_notZero across all virus variables:

\(\text{fracVars_notZero__WeekXX}=\frac{\text{NumberOfVirusVariables__NotZero}_{WeekXX}}{TotalNumberOfVirusVariables}\)

• each fracVars_notZero__WeekXX has range of 0-1

3. Combine tp_score__WeekXX (step 1.) & fracVars_notZero__WeekXX (step 2.)

\(\text{tp_comb_score__WeekXX}=\frac{\text{tp_score__WeekXX }+\text{ fracVars_notZero__WeekXX}}{2}\)

• each tp_comb_score__WeekXX has range of 0-1

Then, for each animal:

Generate composite “timepoint-derived” scores:

4. Average tp_comb_score (from step 3.) across timepoints

\(\text{tp_comb_scores__Mean}=\frac{\sum_{i=Baseline}^{n=Week21}{\text{tp_comb_score}_{i}}}{TotalNumberOfTimepoints}\)

5. Calculate fracTPscores_notZero (from step 3.) across timepoints

\(\text{fracTPscores_notZero}=\frac{\sum_{i=Baseline}^{n=Week21}{\text{tp_comb_score__NotZero}_{i}}}{TotalNumberOfTimepoints}\)

6. Combine tp_comb_scores__Mean (step 4.) & fracTPscores_notZero (step 5.)

\(\text{TP_COMBO}=\frac{\text{tp_comb_scores__Mean } + \text{ fracTPscores_notZero}}{2}\)

Now, for each virus variable, animal:

7. Calculate var_score across all timepoints:

\(\text{e.g. var_score__p24_titer}=\frac{\text{ log}_{10}(\text{p24_titer}_{\text{Baseline}}) /log_{10}(\text{p24_titer}_{MAX}) \text{ } + \text{ log}_{10}(\text{p24_titer}_{\text{Week03}}) /log_{10}(\text{p24_titer}_{MAX}) \text{ } + \text{ ... } + \text{ log}_{10}(\text{p24_titer}_{\text{Week21}}) /log_{10}(\text{p24_titer}_{MAX})}{TotalNumberOfTimepoints}\)

• each p24_titer has range of 0-1
• each var_score__p24_titer has range of 0-1

8. Calculate fracTPs_notZero across all timepoints:

\(\text{e.g. fracTPs_notZero__p24_titer}=\frac{\text{NumberOfTimepoints__NotZero}_{p24_titer}}{TotalNumberOfTimepoints}\)

• each fracTPs_notZero__p24_titer has range of 0-1

9. Combine var_score__p24_titer (step 7.) & fracTPs_notZero__p24_titer (step 8.)

\(\text{e.g. var_comb_score__WeekXX}=\frac{\text{var_score__p24_titer }+\text{ fracTPs_notZero__p24_titer}}{2}\)

• each var_comb_score__p24_titer has range of 0-1

Generate composite “virus variable-derived” scores:

10. Average var_comb_score (from step 9.) across virus variables

\(\text{var_comb_scores__Mean}=\frac{\sum_{i=\text{gag_PCR}}^{n=\text{p24_titer}}{\text{var_comb_score}_{i}}}{TotalNumberOfVirusVariables}\)

11. Calculate fracVARscores_notZero (from step 9.) across timepoints

\(\text{fracVARscores_notZero}=\frac{\sum_{i=\text{gag_PCR}}^{n=\text{p24_titer}}{\text{var_comb_score__NotZero}_{i}}}{TotalNumberOfVirusVariables}\)

12. Combine var_comb_scores__Mean (step 10.) & fracVARscores_notZero (step 11.)

\(\text{VAR_COMBO}=\frac{\text{var_comb_scores__Mean } + \text{ fracVARscores_notZero}}{2}\)

And finally combine “timepoint-derived” & “virus variable-derived” scores:

13. Combine tp_comb_scores__Mean (step 4.) & var_comb_scores__Mean (step 10.)

\(\text{Mean}=\frac{\text{tp_comb_scores__Mean } + \text{ var_comb_scores__Mean}}{2}\)

14. Combine fracTPscores_notZero (step 5.) & fracVARscores_notZero (step 11.)

\(\text{fracScores_notZero}=\frac{\text{fracTPscores_notZero } + \text{ fracVARscores_notZero}}{2}\)

15. Combine TP_COMBO (step 6.) & VAR_COMBO (step 12.)

\(\text{COMBO}=\frac{\text{TP_COMBO } + \text{ VAR_COMBO}}{2}\)

Fig 2d heatmap

timepoint-derived scores from step 3. and their combinations from steps 7-9.

plus

variable-derived scores from step 6. and their combinations from steps 10-12.

plus

combinations of timepoint-derived and variable-derived scores (steps 13-15.)

Fig 2e boxplot

Triple vs No depletion: Day 0 broad cell populations (Supp Fig 2)

red p value: p < 0.05, left timepoint/group is higher than right timepoint/group

blue p value: p < 0.05, left timepoint/group is lower than right timepoint/group

black p value: p > 0.05

HTLV-1ACoI-L vs HTLV-1A_WT: broad cell populations (Supp Fig 4) , including no depletion

only p < 0.05 are shown for clarity

red p value: left timepoint/group is higher than right timepoint/group

blue p value: left timepoint/group is lower than right timepoint/group

Cell subsets Differences between groups (Fig 3, Supp Fig 6, Supp Fig 7)

Fig 3a DCs heatmap

Blood

untrimmed

ROWSCALED

trimmed, p < 0.05

ROWSCALED

Table

BAL

untrimmed

ROWSCALED

trimmed, p < 0.05

ROWSCALED

Table

Scatterplots

Blood

alphabetical

by Dir

BAL

alphabetical

by Dir

Scatterplots with box

BAL free y

BAL same y

Blood free y

Blood same y

Fig 3b Monocytes heatmap

Blood

untrimmed

ROWSCALED

trimmed, p < 0.05

ROWSCALED

Table

BAL

untrimmed

ROWSCALED

trimmed, p < 0.05

ROWSCALED

Table

Scatterplots

Blood

alphabetical

by Dir

BAL

alphabetical

by Dir

Scatterplots with box

BAL free y

BAL same y

Blood free y

Blood same y

Fig 3c Neutrophils heatmap

Blood

untrimmed

ROWSCALED

trimmed, p < 0.05

ROWSCALED

Table

BAL

untrimmed

ROWSCALED

trimmed, p < 0.05

ROWSCALED

Table

Scatterplots

Blood

alphabetical

by Dir

BAL

alphabetical

by Dir

Scatterplots with box

BAL free y

BAL same y

Blood free y

Blood same y

Supp Fig X Alveolar Macrophages heatmap

BAL

untrimmed

ROWSCALED

trimmed, p < 0.05

ROWSCALED

Table

Scatterplots

BAL

alphabetical

by Dir

Scatterplots with box

BAL free y

BAL same y

Cytokine/chemokine Differences between groups (Fig 4, Supp Fig 8, Supp Fig 9)

Fig 4abc Cytokines heatmaps

Blood

untrimmed

ROWSCALED

trimmed, p < 0.05

ROWSCALED

Table

BAL

untrimmed

ROWSCALED

trimmed, p < 0.05

ROWSCALED

Table

Scatterplots with box (Supp Fig 8)

BAL free y

BAL same y

Blood free y

Blood same y

Cytokines: vs No Depletion (Supp Fig 9)

Blood

untrimmed

ROWSCALED

trimmed, p < 0.05

ROWSCALED

Table

BAL

untrimmed

ROWSCALED

trimmed, p < 0.05

ROWSCALED

Table

Associations between sig different cytokines & cell subsets (Fig 4de)

Animals in correlations: 15P044, 17P008, 17P039, 17P062, 18C266, DG8Z, DG8i, DGX3, DHF6, RA6, RH5, RKF, TMN, TRE, TZW

calculate comparisons between CellSubsets in BAL vs ChiVsWT_Cytokines_inBAL : CCL19xx__week12, CCL3xx__week12, FLT3LGxx__week21, IL15xx__week21, IL18xx__week21, IL1Bxx__week05, IL1Bxx__week21, IL33xx__week21, MMP1xx__week05, MMP12xx__week00

calculate comparisons between CellSubsets in Blood vs ChiVsWT_Cytokines_inBAL : CCL19xx__week12, CCL3xx__week12, FLT3LGxx__week21, IL15xx__week21, IL18xx__week21, IL1Bxx__week05, IL1Bxx__week21, IL33xx__week21, MMP1xx__week05, MMP12xx__week00

calculate comparisons between CellSubsets in Blood vs ChiVsWT_Cytokines_inBlood : CCL13xx__week12, CCL2xx__week12, IL33xx__week12, IL6xx__week05, IL7xx__week00

calculate comparisons between CellSubsets in BAL vs ChiVsWT_Cytokines_inBlood : CCL13xx__week12, CCL2xx__week12, IL33xx__week12, IL6xx__week05, IL7xx__week00

0.001

compile associations with p < 0.001

0.01

compile associations with p < 0.01

Virus in lung (Figure 6)

including TiT

by Lobe, Assay

by Assay, Lobe

excluding TiT

by Lobe, Assay

by Assay, Lobe

Olink in vitro (Supp Fig 13c)

Heatmaps

untrimmed

ROWSCALED

trimmed, p < 0.05

ROWSCALED

Table

Scatterplots

CCL11

CCL13

CCL19

CCL2

CCL3

CCL4

CCL7

CCL8

CSF1

CSF2

CSF3

CXCL10

CXCL11

CXCL12

CXCL8

CXCL9

EGF

FLT3LG

HGF

IFNG

IL10

IL13

IL15

IL17A

IL17C

IL17F

IL18

IL1B

IL2

IL27

IL33

IL4

IL6

IL7

LTA

MMP1

MMP12

OLR1

OSM

TGFA

TNF

TNFSF10

TNFSF12

TSLP

VEGFA

Data Tables

Compiled raw data

Excel spreadsheet of RAW datasets

plotted values

Excel spreadsheet of plotted

Stats

Excel spreadsheet of stats

Session info

## R version 4.4.1 (2024-06-14)
## Platform: aarch64-apple-darwin20
## Running under: macOS 15.5
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: America/New_York
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] rstatix_0.7.2      janitor_2.2.0      geepack_1.3.10     MESS_0.5.12       
##  [5] ggalluvial_0.12.5  pander_0.6.5       strex_2.0.1        data.table_1.17.2 
##  [9] factoextra_1.0.7   corrplot_0.95      colorspace_2.1-1   RColorBrewer_1.1-3
## [13] officer_0.6.6      flextable_0.9.6    ggplotify_0.1.2    plotly_4.10.4     
## [17] ggrepel_0.9.6      ggbeeswarm_0.7.2   downloadthis_0.3.3 DT_0.33           
## [21] gridExtra_2.3      gtsummary_1.7.2    kableExtra_1.4.0   broom_1.0.7       
## [25] knitr_1.50         ggpubr_0.6.0       dendsort_0.3.4     pheatmap_1.0.12   
## [29] cowplot_1.1.3      lubridate_1.9.4    forcats_1.0.0      stringr_1.5.1     
## [33] dplyr_1.1.4        purrr_1.0.4        readr_2.1.5        tidyr_1.3.1       
## [37] tibble_3.2.1       ggplot2_3.5.2      tidyverse_2.0.0    readxl_1.4.3      
## [41] openxlsx_4.2.8    
## 
## loaded via a namespace (and not attached):
##  [1] geeM_0.10.1             rstudioapi_0.17.1       jsonlite_2.0.0         
##  [4] magrittr_2.0.3          farver_2.1.2            rmarkdown_2.29         
##  [7] fs_1.6.6                ragg_1.4.0              vctrs_0.6.5            
## [10] memoise_2.0.1           askpass_1.2.1           htmltools_0.5.8.1      
## [13] haven_2.5.4             curl_6.2.2              cellranger_1.1.0       
## [16] Formula_1.2-5           gridGraphics_0.5-1      sass_0.4.10            
## [19] bslib_0.9.0             htmlwidgets_1.6.4       cachem_1.1.0           
## [22] gt_0.10.1               uuid_1.2-1              mime_0.13              
## [25] lifecycle_1.0.4         pkgconfig_2.0.3         Matrix_1.7-0           
## [28] R6_2.6.1                fastmap_1.2.0           snakecase_0.11.1       
## [31] shiny_1.11.0            digest_0.6.37           textshaping_1.0.1      
## [34] crosstalk_1.2.1         labeling_0.4.3          timechange_0.3.0       
## [37] httr_1.4.7              abind_1.4-8             compiler_4.4.1         
## [40] fontquiver_0.2.1        withr_3.0.2             backports_1.5.0        
## [43] carData_3.0-5           ggsignif_0.6.4          MASS_7.3-60.2          
## [46] openssl_2.3.2           gfonts_0.2.0            tools_4.4.1            
## [49] vipor_0.4.7             beeswarm_0.4.0          zip_2.3.2              
## [52] httpuv_1.6.16           clipr_0.8.0             glue_1.8.0             
## [55] promises_1.3.2          grid_4.4.1              checkmate_2.3.2        
## [58] ggformula_0.12.0        generics_0.1.4          gtable_0.3.6           
## [61] labelled_2.13.0         tzdb_0.5.0              hms_1.1.3              
## [64] xml2_1.3.8              car_3.1-3               pillar_1.10.2          
## [67] yulab.utils_0.1.4       later_1.4.2             lattice_0.22-6         
## [70] tidyselect_1.2.1        fontLiberation_0.1.0    fontBitstreamVera_0.1.1
## [73] svglite_2.1.3           crul_1.4.2              xfun_0.52              
## [76] mosaicCore_0.9.4.0      stringi_1.8.7           lazyeval_0.2.2         
## [79] yaml_2.3.10             evaluate_1.0.3          httpcode_0.3.0         
## [82] gdtools_0.3.7           cli_3.6.5               xtable_1.8-4           
## [85] systemfonts_1.2.3       jquerylib_0.1.4         dichromat_2.0-0.1      
## [88] Rcpp_1.0.14             viridisLite_0.4.2       broom.helpers_1.15.0   
## [91] scales_1.4.0            ggridges_0.5.6          crayon_1.5.3           
## [94] rlang_1.1.6